Synthetic DOmain-Targeted Augmentation (S-DOTA) Improves Model Generalization in Digital Pathology

TL;DR

This paper introduces S-DOTA, a set of synthetic domain-targeted augmentation techniques, that significantly enhance the generalization of digital pathology models across diverse domains and imaging conditions.

Contribution

The study presents two novel S-DOTA methods, CycleGAN-enabled Scanner Transform and targeted Stain Vector Augmentation, demonstrating their effectiveness over traditional calibration in improving model robustness.

Findings

S-DOTA methods outperform ICC Cal and baseline on out-of-distribution data.

S-DOTA maintains comparable in-distribution performance.

Significant improvements in macro-averaged F1 scores across multiple tasks and domains.

Abstract

Machine learning algorithms have the potential to improve patient outcomes in digital pathology. However, generalization of these tools is currently limited by sensitivity to variations in tissue preparation, staining procedures and scanning equipment that lead to domain shift in digitized slides. To overcome this limitation and improve model generalization, we studied the effectiveness of two Synthetic DOmain-Targeted Augmentation (S-DOTA) methods, namely CycleGAN-enabled Scanner Transform (ST) and targeted Stain Vector Augmentation (SVA), and compared them against the International Color Consortium (ICC) profile-based color calibration (ICC Cal) method and a baseline method using traditional brightness, color and noise augmentations. We evaluated the ability of these techniques to improve model generalization to various tasks and settings: four models, two model types (tissue segmentation and cell classification), two loss functions, six labs, six scanners, and three indications (hepatocellular carcinoma (HCC), nonalcoholic steatohepatitis (NASH), prostate adenocarcinoma). We compared these methods based on the macro-averaged F1 scores on in-distribution (ID) and out-of-distribution (OOD) test sets across multiple domains, and found that S-DOTA methods (i.e., ST and SVA) led to significant improvements over ICC Cal and baseline on OOD data while maintaining comparable performance on ID data. Thus, we demonstrate that S-DOTA may help address generalization due to domain shift in real world applications.